Settlement prediction system based on soft soil creep

Abstract

The invention discloses a settlement prediction system based on soft soil creep, which relates to the technical field of settlement prediction, and dynamically calculates stress increment, total stress increment and pore water pressure by collecting the loading rate and underground water level change of a foundation in real time, and further calculates vertical effective stress based on the intermediate variables. Therefore, an accurate void ratio calculation formula is constructed. The systematized process not only improves the precision of settlement prediction, but also enhances the adaptability of the model to the change of actual working conditions. Specifically, through collection and processing of real-time data, the method can quickly respond to changes of foundation conditions, and it is ensured that model parameters are always in the optimal state. Calculating the void ratio by using the initial model parameters and evaluating the difference between a predicted value and an actual observed value in combination with an error function, so as to realize iterative optimization of the initial model parameters and finally obtain a corrected empirical model. The robustness and reliability of the model are remarkably improved, and settlement prediction is more accurate and reliable.

Description

Technical Field

[0001] The invention relates to the technical field of settlement prediction, and in particular to a settlement prediction system based on soft soil creep. Background Art

[0002] Soft soils generally refer to soil types with high water content, low density and high compressibility, such as silt and peat. These soils exhibit significant deformation characteristics when subjected to building or infrastructure loads, especially long-term settlement. This settlement is mainly caused by the gradual discharge of water from the soft soil and the rearrangement of soil particles, a process known as "creep". Buildings and infrastructure on soft soil foundations face greater risks because long-term settlement may cause uneven sinking of the structure, causing cracks, tilting and other problems, seriously affecting the safety and service life of the building.

[0003] In the prior art, the empirical formula method relies on historical data and statistical analysis. By performing regression analysis on the data of existing engineering cases, a relationship model between settlement and related parameters is established, and these models are used to predict the settlement of future projects. However, in the actual construction process, the loading rate of buildings or infrastructure may change due to factors such as construction progress and weather conditions. Rapid loading will lead to higher pore water pressure, slow down the drainage process, and thus increase the settlement time and final settlement. However, the empirical formula often assumes that the loading rate is constant and cannot adapt to this dynamic change. Changes in groundwater levels have an important impact on the settlement of soft soil foundations. Rising groundwater levels will reduce effective stress, increase pore water pressure, and thus increase settlement; conversely, falling groundwater levels will reduce pore water pressure and reduce settlement. Since the empirical formula does not take these dynamic factors into account, the prediction results may have large deviations when the groundwater level fluctuates greatly. Therefore, the empirical formula method is not flexible and accurate enough when facing complex actual construction conditions, and cannot be adjusted and optimized in real time according to actual conditions.

[0004] Therefore, the prior art urgently needs a technical solution for a settlement prediction system based on soft soil creep. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a settlement prediction system based on soft soil creep, which specifically includes the following modules: Initial empirical model building module: used to obtain historical settlement data, historical geological parameters and historical environmental parameters, build an initial empirical model based on the historical settlement data, historical geological parameters and historical environmental parameters, and initialize the model parameters of the initial empirical model to obtain initial model parameters; Dynamic parameter acquisition module: used to collect the real-time loading rate and real-time groundwater level changes of the foundation through sensors; Stress increment calculation module: connected with the dynamic parameter acquisition module, used to calculate the stress increment and total stress increment according to the real-time loading rate; Pore ​​water pressure calculation module: connected with the dynamic parameter acquisition module, the initial empirical model construction module, and the stress increment calculation module, and used to calculate the pore water pressure at the current moment according to the initial model parameters, the real-time groundwater level change, and the total stress increment; Vertical effective stress calculation module: connected with the stress increment calculation module and the pore water pressure calculation module, used to calculate the vertical effective stress at the current moment according to the total stress increment and the pore water pressure; Porosity ratio calculation module: connected with the vertical effective stress calculation module and the initial empirical model construction module, used to calculate the porosity ratio at the current moment according to the vertical effective stress at the current moment and the initial model parameter calculation module; Model correction module: connected with the initial empirical model construction module and the porosity ratio calculation module, used to optimize the initial model parameters according to the porosity ratio to obtain a corrected empirical model; Prediction module: connected with the model correction module, used to predict the settlement of the foundation based on the corrected empirical model.

[0006] Furthermore, the initial model parameters include: initial consolidation coefficient, initial void ratio, initial creep coefficient and initial compression index.

[0007] Furthermore, the stress increment calculation module includes the following submodules: Foundation division submodule: used to divide the foundation area into several target areas; Target area real-time loading rate acquisition submodule: used to obtain the real-time loading rate of each target area through sensors; The time interval determination submodule is used to determine the time interval according to the acquisition cycle of the sensor; Target area stress increment calculation submodule: used to calculate the stress increment of each target area according to the real-time loading rate and time interval of each target area; Total stress increment calculation submodule: used to calculate the total stress increment of the foundation based on the stress increment of each target area.

[0008] Furthermore, the pore water pressure calculation module includes the following submodules: Water level change acquisition submodule: obtains the initial water level and the current water level, and calculates the water level change based on the initial water level and the current water level; Pore ​​water pressure calculation submodule: used to calculate the current pore water pressure based on the initial model parameters, water level change and total stress increment.

[0009] Furthermore, the void ratio calculation module includes the following submodules: Initial vertical effective stress preset submodule: used to preset an initial vertical effective stress; Porosity calculation submodule: used to calculate the porosity according to the initial porosity, initial compression index, vertical effective stress at the current moment, initial vertical effective stress, and initial creep coefficient.

[0010] Furthermore, the model correction module includes: defining an error function for calculating the error value between the predicted value and the actual observed value, calculating the porosity through the initial model parameters, and inputting the initial empirical model to obtain the predicted value, calculating the error function value according to the predicted value and the actual observed value, adjusting the initial model parameters by the gradient descent method to obtain the minimized error function value, obtaining the optimal parameter combination, replacing the optimal parameter combination with the initial model parameters, and obtaining the corrected empirical model; the optimal parameter combination includes optimizing the creep coefficient, optimizing the consolidation coefficient, optimizing the porosity, and optimizing the compression index.

[0011] Furthermore, the prediction module includes the following submodules: Real-time loading rate acquisition submodule: used to acquire real-time loading rate through sensors; Real-time groundwater level acquisition submodule: used to acquire real-time groundwater level; Soil layer thickness acquisition submodule: used to obtain soil layer thickness; Prediction submodule: It is used to use the real-time loading rate, real-time groundwater level and soil thickness as input data of the modified empirical model to obtain the void ratio, and calculate the settlement through the void ratio, optimized void ratio and soil thickness, and obtain the optimized construction plan according to the settlement.

[0012] Furthermore, the calculation formula of pore water pressure is: ; where u represents the pore water pressure, represents the adjustment factor, represents the total stress increment, represents the initial consolidation coefficient in the initial model parameters, Q represents the water level change, Represents the gradient operator.

[0013] Furthermore, the calculation formula of the void ratio is: ; In the formula, represents the void ratio at the current time t, represents the initial void ratio, represents the initial compression index, represents the initial vertical effective stress, represents the vertical effective stress at the current time t, D represents the initial creep coefficient, T represents the time constant, represents the integration variable.

[0014] The embodiments of the present invention have the following technical effects: The present invention collects the loading rate and groundwater level changes of the foundation in real time, dynamically calculates the stress increment, the total stress increment and the pore water pressure, and further calculates the vertical effective stress based on these intermediate variables, thereby constructing an accurate porosity ratio calculation formula. This systematic process not only improves the accuracy of settlement prediction, but also enhances the adaptability of the model to actual working condition changes. Specifically, through the collection and processing of real-time data, the present invention can quickly respond to changes in foundation conditions and ensure that the model parameters are always in the optimal state. The porosity ratio is calculated using the initial model parameters and the difference between the predicted value and the actual observed value is evaluated in combination with the error function, so as to achieve iterative optimization of the initial model parameters and finally obtain the revised empirical model. This adaptive parameter optimization mechanism significantly improves the robustness and reliability of the model, making the settlement prediction more accurate and reliable. In addition, with the help of the revised empirical model, the settlement of the foundation can be predicted more accurately, which provides a scientific basis for the optimization of the construction plan, reduces the engineering risk and cost increase caused by inaccurate settlement prediction, and greatly improves the overall benefit and safety of the project. At the same time, the design ideas and methods of this system also provide valuable reference and lessons for other similar geological engineering problems, and have broad application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a structural diagram of a settlement prediction system based on soft soil creep provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. 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 belong to the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 As shown, the present invention provides a settlement prediction system based on soft soil creep, comprising the following modules: Initial empirical model construction module: used to obtain historical settlement data, historical geological parameters and historical environmental parameters, build an initial empirical model based on the historical settlement data, historical geological parameters and historical environmental parameters, and initialize the model parameters of the initial empirical model to obtain the initial model parameters; the initial model parameters include: initial consolidation coefficient, initial porosity, initial creep coefficient and initial compression index.

[0019] Collect historical settlement data, historical geological parameters and historical environmental parameters of the foundation in the past year. Historical geological parameters may include: soil thickness, soil type, soil density and soil moisture content, etc. Historical environmental parameters include initial groundwater level, average rainfall, average temperature, etc.

[0020] The historical settlement data include the historical settlement amount of each settlement monitoring point at each historical moment.

[0021] Based on the above historical data and initial model parameters, the initial empirical model is constructed using classical consolidation theory: ; S(t) represents the settlement at the current time t, H0 represents the initial soil layer thickness, e0 represents the initial porosity, and e(t) represents the porosity at the current time t.

[0022] According to the soil layer type, the initial porosity is determined by consulting relevant literature or experimental data. For example, the initial porosity of clay is 0.8. Similarly, through triaxial compression test or consulting literature, the initial compression modulus is 5 MPa. Through permeability test, the initial permeability coefficient is 1×10⁻ 6 m / s. Through the consolidation test, the initial consolidation coefficient is 2×10⁻ 7 m² / s. Through the compression test, the initial compression index was 0.2.

[0023] Dynamic parameter acquisition module: used to collect the real-time loading rate of the foundation and the real-time groundwater level changes through sensors.

[0024] Stress increment calculation module: connected with the dynamic parameter acquisition module, used to calculate the stress increment and the total stress increment according to the real-time loading rate.

[0025] Stress increment calculation module, including the following sub-modules: Foundation division submodule: used to divide the foundation area into several target areas.

[0026] Target area real-time loading rate acquisition submodule: used to obtain the real-time loading rate of each target area through sensors.

[0027] Time interval determination submodule: used to determine the time interval according to the collection cycle of the sensor.

[0028] Target area stress increment calculation submodule: used to calculate the stress increment of each target area according to the real-time loading rate and time interval of each target area.

[0029] ; in, represents the stress increment of the i-th target area, represents the real-time loading rate of the i-th target area, Represents a time interval.

[0030] Total stress increment calculation submodule: used to calculate the total stress increment of the foundation based on the stress increment of each target area.

[0031] ; n represents the total number of target areas, Represents the total stress increase in the foundation.

[0032] Pore ​​water pressure calculation module: connected with the dynamic parameter acquisition module, the initial empirical model construction module, and the stress increment calculation module, it is used to calculate the pore water pressure at the current moment according to the initial model parameters, the real-time groundwater level changes, and the total stress increment.

[0033] The pore water pressure calculation module includes the following submodules: Water level change acquisition submodule: obtains the initial water level and the current water level, and calculates the water level change based on the initial water level and the current water level; Pore ​​water pressure calculation submodule: used to calculate the current pore water pressure based on the initial model parameters, water level change and total stress increment.

[0034] ; where u represents the pore water pressure, represents the adjustment factor, represents the total stress increment, represents the initial consolidation coefficient in the initial model parameters, Q represents the water level change, Represents the gradient operator.

[0035] Through the pore water pressure calculation module, we can monitor the groundwater level changes and total stress increments in real time, and use these data to accurately calculate the pore water pressure at the current moment. Specifically, the module first obtains the initial water level and the water level at the current moment through the water level change acquisition submodule, and then calculates the water level change. Subsequently, the pore water pressure calculation submodule uses the water level change and the total stress increment, combined with parameters such as the adjustment coefficient and consolidation coefficient, to solve the change in pore water pressure through the gradient operator and the water level change. This process can not only reflect the dynamic changes of pore water pressure in the foundation in real time, but also provide key inputs for subsequent vertical effective stress calculations, thereby improving the accuracy and reliability of settlement predictions.

[0036] Vertical effective stress calculation module: connected with the stress increment calculation module and the pore water pressure calculation module, used to calculate the vertical effective stress at the current moment according to the total stress increment and the pore water pressure.

[0037] The vertical effective stress is obtained by subtracting the total stress increment from the pore water pressure.

[0038] Porosity ratio calculation module: connected with the vertical effective stress calculation module and the initial empirical model construction module, and used to calculate the porosity ratio at the current moment according to the vertical effective stress at the current moment and the initial model parameter calculation module.

[0039] The void ratio calculation module includes the following submodules: Initial vertical effective stress preset submodule: used to preset an initial vertical effective stress; For uniformly distributed soil layers, the initial vertical effective stress is calculated according to the static earth pressure theory: ;

[0040] In the formula, represents the initial vertical effective stress, represents the effective weight per unit volume of the soil layer, and z represents the depth of the soil layer.

[0041] For saturated soils, the effective weight per unit volume is calculated using the following formula: ; In the formula, represents the weight of the soil layer in a fully saturated state. Represents the weight of water, specifically the actual weight of the part of the soil under water after being affected by buoyancy. .

[0042] Soil samples are collected from the site and the moisture content of the soil is measured by drying. The wet density of the soil is measured by ring knife method or other methods. The saturated density is calculated based on the wet density and moisture content. The saturated density is preferably 13kN / m 3 .

[0043] Porosity calculation submodule: used to calculate the porosity according to the initial porosity, initial compression index, vertical effective stress at the current moment, initial vertical effective stress, and initial creep coefficient.

[0044] ; In the formula, represents the void ratio at the current time t, represents the initial void ratio, represents the initial compression index, represents the initial vertical effective stress, represents the vertical effective stress at the current time t, D represents the initial creep coefficient, T represents the time constant, represents the integration variable.

[0045] Although the porosity ratio can be obtained through laboratory measurements, in actual situations, foundation conditions are complex and changeable, and laboratory measurements may not be able to fully reflect the on-site conditions. In addition, through real-time data and dynamic adjustment of model parameters, a more accurate prediction of foundation behavior can be achieved without increasing excessive experimental costs. Specifically: abandoning the traditional method based on the ratio of the pore volume to the volume of solid particles in the soil, this application uses a complex formula to calculate the porosity ratio. Its necessity lies in the ability to more accurately simulate and predict foundation behavior in actual engineering scenarios. The initial porosity ratio provides basic data for the foundation in a stress-free state; the compression index reflects the compression characteristics of the soil under different pressure conditions. Adjusting the void ratio can dynamically reflect the changes in the foundation when it is under load; the creep coefficient D time constant describes the deformation characteristics of the soil over time, and the integral part takes into account the creep effect of the soil under long-term load. Compared with simple laboratory measurements, this method can not only capture the instantaneous pressure response, but also simulate the gradual changes over a long period of time, thereby providing a more accurate and comprehensive prediction of foundation settlement, greatly improving the reliability and applicability of the model, especially under complex geological conditions, such meticulous calculations are particularly necessary.

[0046] By comprehensively considering the initial void ratio, initial compression index, the relationship between the vertical effective stress at the current moment and the initial vertical effective stress, and the creep characteristics of the soil, an accurate prediction of the void ratio of the foundation that changes over time under different load conditions is achieved. It can accurately reflect the compression behavior of the soil when subjected to external loads and its creep effect that evolves over time, thereby providing a scientific basis for settlement prediction. Specifically, dynamically monitoring and adjusting the changes in void ratio based on real-time data, and then predicting the amount of foundation settlement, is crucial for assessing the long-term stability of buildings or infrastructure. In addition, through the accurate prediction of the changes in void ratio, the engineering team can take corresponding measures in a timely manner, such as optimizing the construction plan or implementing foundation reinforcement, to ensure the smooth progress of the project and the safety of the structure. Therefore, this calculation method not only improves the accuracy of settlement prediction, but also provides strong support for practical engineering applications.

[0047] Model correction module: connected with the initial empirical model construction module and the porosity calculation module, used to optimize the initial model parameters according to the porosity to obtain a corrected empirical model.

[0048] Define an error function, which is a mean square error function, to calculate the error between the predicted value and the actual observed value. Calculate the porosity by using the initial model parameters, and input the initial empirical model to obtain the predicted value. Calculate the error function value based on the predicted value (the predicted value is the porosity calculated by the porosity calculation submodule and substituted into the settlement at the current time t calculated in the initial empirical model) and the actual observed value (the actual observed value refers to the settlement obtained by field measurement). Use the gradient descent method to adjust the initial model parameters to obtain the minimized error function value, obtain the optimal parameter combination, replace the optimal parameter combination with the initial model parameters, and obtain the modified empirical model. The optimal parameter combination includes optimizing the consolidation coefficient, optimizing the porosity, optimizing the compression index, and optimizing the creep coefficient.

[0049] Prediction module: connected with the model correction module, used to predict the settlement of the foundation based on the corrected empirical model.

[0050] The prediction module includes the following submodules: Real-time loading rate acquisition submodule: used to acquire real-time loading rate through sensors; Real-time groundwater level acquisition submodule: used to acquire real-time groundwater level; Soil layer thickness acquisition submodule: used to obtain soil layer thickness; Prediction submodule: It is used to use the real-time loading rate, real-time groundwater level and soil layer thickness as input data of the modified empirical model, and calculate the porosity through the porosity calculation formula (in this formula, the initial consolidation coefficient, initial porosity, initial compression index and initial creep coefficient are all the optimized consolidation coefficient, optimized porosity, optimized compression index and optimized creep coefficient in the optimal parameter combination), and calculate the settlement through the porosity, optimized porosity and soil layer thickness, and obtain the optimized construction plan according to the settlement.

[0051] If the predicted settlement is relatively large (in this embodiment, settlement exceeding 5 meters is considered to be relatively large), the following optimized construction scheme is used for construction optimization: Increase foundation depth or width: Increase the depth or width of the foundation to ensure structural stability.

[0052] Use pile foundation: Use pile foundation to reduce the effects of settlement, especially for tall buildings or structures that are sensitive to settlement.

[0053] Preloading treatment: Preloading the foundation before construction can cause a certain amount of settlement in advance, thereby reducing the risk of uneven settlement in the later stage.

[0054] Regular monitoring: Regularly monitor foundation settlement during and after construction, and adjust construction strategies or take reinforcement measures in a timely manner.

[0055] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0056] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", 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 a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. 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 an indirect connection through an intermediate medium, or it can be a connection between the 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.

[0057] 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 by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A settlement prediction system based on soft soil creep, characterized in that: Includes the following modules: Initial empirical model building module: used to obtain historical settlement data, historical geological parameters and historical environmental parameters, build an initial empirical model based on the historical settlement data, historical geological parameters and historical environmental parameters, and initialize the model parameters of the initial empirical model to obtain initial model parameters; Dynamic parameter acquisition module: used to collect the real-time loading rate and real-time groundwater level changes of the foundation through sensors; Stress increment calculation module: connected with the dynamic parameter acquisition module, used to calculate the stress increment and total stress increment according to the real-time loading rate; Pore ​​water pressure calculation module: connected with the dynamic parameter acquisition module, the initial empirical model construction module, and the stress increment calculation module, and used to calculate the pore water pressure at the current moment according to the initial model parameters, the real-time groundwater level change, and the total stress increment; Vertical effective stress calculation module: connected with the stress increment calculation module and the pore water pressure calculation module, used to calculate the vertical effective stress at the current moment according to the total stress increment and the pore water pressure; Porosity ratio calculation module: connected with the vertical effective stress calculation module and the initial empirical model construction module, used to calculate the porosity ratio at the current moment according to the vertical effective stress at the current moment and the initial model parameters; Model correction module: connected with the initial empirical model construction module and the porosity ratio calculation module, used to optimize the initial model parameters according to the porosity ratio to obtain a corrected empirical model; Prediction module: connected with the model correction module, used to predict the settlement of the foundation based on the corrected empirical model.

2. A settlement prediction system based on soft soil creep according to claim 1, characterized in that: The initial model parameters include: initial consolidation coefficient, initial porosity, initial creep coefficient and initial compression index.

3. The settlement prediction system based on soft soil creep according to claim 1, characterized in that: Stress increment calculation module, including the following sub-modules: Foundation division submodule: used to divide the foundation area into several target areas; Target area real-time loading rate acquisition submodule: used to obtain the real-time loading rate of each target area through sensors; The time interval determination submodule is used to determine the time interval according to the acquisition cycle of the sensor; Target area stress increment calculation submodule: used to calculate the stress increment of each target area according to the real-time loading rate and time interval of each target area; Total stress increment calculation submodule: used to calculate the total stress increment of the foundation based on the stress increment of each target area.

4. A settlement prediction system based on soft soil creep according to claim 2, characterized in that: The pore water pressure calculation module includes the following submodules: Water level change acquisition submodule: obtains the initial water level and the current water level, and calculates the water level change based on the initial water level and the current water level; Pore ​​water pressure calculation submodule: used to calculate the current pore water pressure based on the initial model parameters, water level change and total stress increment.

5. The settlement prediction system based on soft soil creep according to claim 2, characterized in that: The void ratio calculation module includes the following submodules: Initial vertical effective stress preset submodule: used to preset an initial vertical effective stress; Porosity calculation submodule: used to calculate the porosity according to the initial porosity, initial compression index, vertical effective stress at the current moment, initial vertical effective stress, and initial creep coefficient.

6. The settlement prediction system based on soft soil creep according to claim 1, characterized in that: The model correction module includes: defining an error function for calculating the error value between the predicted value and the actual observed value, calculating the porosity through the initial model parameters, and inputting the initial empirical model to obtain the predicted value, calculating the error function value according to the predicted value and the actual observed value, adjusting the initial model parameters by the gradient descent method to obtain the minimized error function value, obtaining the optimal parameter combination, replacing the optimal parameter combination with the initial model parameters, and obtaining the corrected empirical model; the optimal parameter combination includes optimizing the creep coefficient, optimizing the consolidation coefficient, optimizing the porosity, and optimizing the compression index.

7. A settlement prediction system based on soft soil creep according to claim 6, characterized in that: The prediction module includes the following submodules: Real-time loading rate acquisition submodule: used to acquire real-time loading rate through sensors; Real-time groundwater level acquisition submodule: used to acquire real-time groundwater level; Soil layer thickness acquisition submodule: used to obtain soil layer thickness; Prediction submodule: It is used to use the real-time loading rate, real-time groundwater level and soil thickness as input data of the modified empirical model to obtain the void ratio, and calculate the settlement through the void ratio, optimized void ratio and soil thickness, and obtain the optimized construction plan according to the settlement.

8. The settlement prediction system based on soft soil creep according to claim 4, characterized in that: The calculation formula for pore water pressure is: ; where u represents the pore water pressure, represents the adjustment factor, represents the total stress increment, represents the initial consolidation coefficient in the initial model parameters, Q represents the water level change, Represents the gradient operator.

9. The settlement prediction system based on soft soil creep according to claim 5, characterized in that: The calculation formula of void ratio is: ; In the formula, represents the void ratio at the current time t, represents the initial void ratio, represents the initial compression index, represents the initial vertical effective stress, represents the vertical effective stress at the current time t, D represents the initial creep coefficient, T represents the time constant, represents the integration variable.

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