A prediction method and system for excavation settlement values of bilateral expansion of subway

Abstract

The present invention relates to the technical field of expansion compensation models, and in particular to a method and system for predicting the excavation settlement value of subway double-sided expansion, including constructing a geological exploration sub-model and an expansion design sub-model by collecting initial monitoring data and historical settlement data. By analyzing the influence degree between each sub-model, determining the corresponding relationship, and establishing a test environment, the influence factor of variables on settlement is calculated. Based on the influence factor and degree, a test model is established and the first settlement prediction model is screened out. The model is optimized by combining historical data to form a second settlement prediction model for real-time settlement prediction and compensation. The present invention integrates multiple models, improves the accuracy of settlement prediction, optimizes construction parameters, realizes personalized settlement control, reduces construction risks, ensures construction safety and the stability of the surrounding environment, and at the same time improves construction efficiency and shortens the construction period.

Description

Technical Field

[0001] The present invention relates to the technical field of expansion and construction compensation models, and in particular to a method and system for predicting excavation settlement values for two-sided expansion and construction of a subway. Background Art

[0002] With the acceleration of urbanization, subways, as a vital component of urban transportation, are receiving increasing attention for their construction and expansion. Bilateral subway expansion refers to the expansion of existing subway lines on both sides to increase capacity and alleviate traffic pressure. However, during bilateral subway expansion, construction activities such as earthwork excavation, groundwater management, and backfilling can cause settlement in the subway and underground structures. This not only threatens construction safety but can also cause damage to the surrounding environment. Therefore, how to effectively control settlement during bilateral subway expansion has become a technical challenge that needs to be addressed urgently.

[0003] Currently, several technical approaches are being widely adopted to address settlement issues during the bilateral expansion of subway lines. For example, geological surveys are used to understand the distribution and properties of underground soil layers, providing basic data for construction design. On-site monitoring equipment is used to monitor settlement of subway lines and underground structures in real time, enabling timely identification and resolution of any issues. Advanced construction techniques and management measures, such as layered excavation, water management, and reinforcement, are employed to minimize the impact of construction on subway lines and underground structures. Furthermore, some scholars and research institutions are dedicated to developing new settlement prediction and control methods to improve the accuracy and effectiveness of settlement control.

[0004] Although existing technologies have achieved certain results in controlling settlement during the bilateral expansion of subways, there are still some technical problems that need to be solved. First of all, how to accurately predict the amount of settlement is one of the main challenges currently faced. Due to the influence of multiple factors such as geological conditions, construction parameters, and environmental factors, the prediction of settlement has great uncertainty and complexity. Secondly, how to adjust the construction parameters in a timely and effective manner according to the prediction results to reduce the impact of settlement is also a difficult problem. In the actual construction process, the adjustment of construction parameters is often restricted by multiple factors, such as construction period, cost, technical feasibility, etc., which makes the implementation of settlement control more difficult. Finally, how to optimize the settlement adjustment under complex geological and construction conditions is also a problem that needs to be solved urgently. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a method for predicting excavation settlement values for two-sided subway expansion, comprising:

[0006] Step S1, collecting initial monitoring data of the subway extension area, and obtaining historical settlement data and extension design data of the subway extension area, the initial monitoring data including geological survey data and on-site monitoring data;

[0007] Step S2, build single models according to individual data types of the initial monitoring data, and establish several geological exploration sub-models and expansion design sub-models respectively;

[0008] Step S3, determine the influence degree of each geological exploration sub-model on the expansion design sub-model, and determine the corresponding relationship between each geological exploration sub-model and the expansion design sub-model according to each influence degree;

[0009] Step S4, in the models included in the same corresponding relationship, establish a test environment for superimposing each variable, and determine the influence factors of the variables in each sub-model on the on-site monitoring data;

[0010] Step S5, re-establish several test models respectively according to the influence degree and influence factors, and perform simulation prediction on each on-site monitoring data, and determine the first settlement prediction model according to the prediction results;

[0011] Step S6, analyze and predict the first settlement prediction model using the historical settlement data, and adjust the influence weights of the variables in each test model in the first settlement prediction model according to the gap between the prediction results and the on-site monitoring data to obtain the second settlement prediction model;

[0012] Step S7, use the second settlement prediction model to simulate to obtain the subway settlement prediction value, and perform compensation according to the subway settlement prediction value.

[0013] As a preferred technical solution of the excavation settlement value prediction method for subway bilateral expansion, the geological exploration data includes soil type, soil density, and soil internal friction angle;

[0014] The on-site monitoring data includes subway settlement monitoring data, horizontal displacement at the top of the retaining pile, and vertical displacement at the top of the retaining pile;

[0015] The expansion design data includes foundation pit size and retaining structure form.

[0016] As a preferred technical solution of the excavation settlement value prediction method for subway bilateral expansion, the geological exploration sub-models include soil type sub-model, soil density sub-model, and soil internal friction angle sub-model;

[0017] The expansion design sub-models include foundation pit size sub-model and retaining structure sub-model.

[0018] As a preferred technical solution of the excavation settlement value prediction method for subway bilateral expansion, in the step S3, determining the corresponding relationship between each geological exploration sub-model and the expansion design sub-model includes:

[0019] Step S31: According to the historical settlement data, perform data sequence accumulation on a single type of geological exploration sub-model to obtain the corresponding geological exploration update data sequence;

[0020] Step S32: According to the same historical settlement data, perform data sequence accumulation on a single type of expansion design sub-model to obtain the corresponding expansion design update data sequence;

[0021] Step S33: Calculate the influence degree between the geological exploration update data sequence and the expansion design update data sequence;

[0022] Step S34: Repeat Step S33 to determine the influence degree of each geological exploration sub-model and each expansion design sub-model, and determine the corresponding relationship between each geological exploration sub-model and the expansion design sub-model according to the influence degree between each geological exploration update data sequence and the expansion design update data sequence.

[0023] As an optimized technical solution of the excavation settlement value prediction method for subway double-sided expansion, in Step S4, the influence factors of the variables in the sub-models of the current corresponding relationship on the on-site monitoring data include:

[0024] Determine the test variables in the current sub-model, take them and the variables as constants, adjust the test variables, and obtain the actual values and the change amounts of each on-site monitoring data in the historical settlement data, and calculate the influence factors of the current test variables on the on-site monitoring data;

[0025] Adjust the test variables and constants in the current sub-model, and determine the influence factors of the new test variables on the on-site monitoring data again;

[0026] Repeat the process of replacing variables to calculate the influence factors until the influence factors of all variables in the sub-models of the current corresponding relationship are determined.

[0027] As an optimized technical solution of the excavation settlement value prediction method for subway double-sided expansion, in Step S6, determining the first settlement prediction model according to the prediction results includes:

[0028] Use each test model to predict all types of on-site monitoring data;

[0029] According to the prediction results of each on-site monitoring data, select the test model corresponding to the smallest prediction error of each on-site monitoring data as the first settlement prediction model.

[0030] As an optimized technical solution of the excavation settlement value prediction method for subway double-sided expansion, in Step S6, adjusting the influence weights of the variables in each test model in the first settlement prediction model includes:

[0031] Adjust the influence weights of the variables in the first settlement prediction model according to the principle of minimizing the simulation error;

[0032] The principle of minimizing the simulation error is that the error rate of the prediction result of the first settlement prediction model determined by using the influence weights of the adjusted variables is the smallest.

[0033] As an optimal technical solution of the excavation settlement value prediction method for subway double-sided expansion, in the step S7, if the subway settlement prediction value exceeds the standard prediction value, select the test models with the smallest prediction error in each on-site monitoring data for separate prediction, and select the maximum prediction value of each on-site monitoring data as the final subway settlement prediction value, and use the backfill and axial force servo system for compensation.

[0034] The present invention also provides a system for an excavation settlement value prediction method applied to subway double-sided expansion, including:

[0035] An information collection module, which is used to collect the initial monitoring data of the subway expansion area, and obtain the historical settlement data and expansion design data of the subway expansion area;

[0036] An information processing module, which is connected to the information collection module, and is used to establish the geological exploration sub-model and the expansion design sub-model, determine the influence degree of each geological exploration sub-model on the expansion design sub-model, and determine the corresponding relationship between each geological exploration sub-model and the expansion design sub-model;

[0037] A model establishment module, which is respectively connected to the information collection module and the information processing module, and is used to establish a test environment for superimposing various variables, determine the influence factors of the variables in each sub-model on the on-site monitoring data, establish several test models and conduct simulation predictions to determine the first settlement prediction model;

[0038] A model optimization module, which is respectively connected to the information collection module and the model establishment module, and is used to analyze and predict the first settlement prediction model according to the historical settlement data, and adjust the influence weights of the variables in each test model in the first settlement prediction model according to the gap between the prediction result and the on-site monitoring data to obtain a second settlement prediction model;

[0039] A compensation module, which is respectively connected to the model optimization module and the information collection module, and is used to compensate according to the subway settlement prediction value simulated by the second settlement prediction model.

[0040] One or more of the above technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0041] By integrating multiple models, the present invention can make full use of historical settlement data and initial monitoring data, improve the accuracy and reliability of settlement prediction, help to identify potential settlement risks in advance, and provide a basis for timely adjustment of construction strategies. Secondly, by establishing an objective function and optimizing various adjustment methods, the present invention can quickly determine the optimal adjustment method after predicting the settlement trend, realize real-time compensation and adjustment of settlement, help to reduce the settlement amount during construction, and ensure construction safety and the stability of the surrounding environment. In addition, the method provided by the present invention can flexibly adjust construction parameters according to different geological conditions, construction environments and settlement prediction results, realize personalized settlement control schemes, enhance the flexibility and adaptability of the construction process, and help to cope with complex and changeable construction challenges. Through accurate prediction and timely adjustment, the present invention helps to reduce construction accidents and damage to the surrounding environment caused by settlement, thereby reducing construction risks and costs. At the same time, the optimized construction plan also helps to improve construction efficiency and shorten the construction period.

[0042] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description 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.

[0044] Figure 1 It is a flowchart of the method for predicting the excavation settlement value of the subway double-sided expansion in the embodiment of the present invention;

[0045] Figure 2 It is a flowchart of determining the corresponding relationship in the embodiment of the present invention;

[0046] Figure 3 It is a flowchart of determining the influencing factors in the embodiment of the present invention;

[0047] Figure 4 It is a schematic structural diagram of the excavation real-time compensation system for the subway double-sided expansion in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the accompanying drawings in the present invention. Apparently, 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 fall within the protection scope of the present invention. The following embodiments are used to illustrate the present invention but cannot be used to limit the scope of the present invention.

[0049] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0050] Please refer to Figure 1 as shown, which is a flowchart of a method for predicting the excavation settlement value of subway bilateral expansion in an embodiment of the present invention; the present invention provides a method for predicting the excavation settlement value of subway bilateral expansion, including:

[0051] Step S1, collect the initial monitoring data of the subway expansion area, and obtain the historical settlement data and expansion design data of the subway expansion area. The initial monitoring data includes geological exploration data and on-site monitoring data;

[0052] Step S2, construct a single model according to the individual data types of the initial monitoring data, and establish several geological exploration sub-models and expansion design sub-models respectively;

[0053] Step S3, determine the influence degree of each geological exploration sub-model on the expansion design sub-model, and determine the corresponding relationship between each geological exploration sub-model and the expansion design sub-model according to each influence degree;

[0054] Step S4, in the models included in the same corresponding relationship, establish a test environment for superimposing each variable, and determine the influence factors of the variables in each sub-model on the on-site monitoring data;

[0055] Step S5, respectively re-establish several test models according to the influence degree and influence factors, and perform simulation prediction on each on-site monitoring data, and determine the first settlement prediction model according to the prediction results;

[0056] Step S6: Analyze and predict the first settlement prediction model using the historical settlement data, and adjust the influence weights of the variables in each test model of the first settlement prediction model according to the gap between the prediction result and the on-site monitoring data to obtain the second settlement prediction model;

[0057] Step S7: Use the second settlement prediction model to perform simulation to obtain the subway settlement prediction value, and perform compensation according to the subway settlement prediction value.

[0058] In this embodiment, the number of the geological exploration sub-models and the number of the expansion design sub-models are respectively the same as the number of data types included in the geological exploration data and the expansion design data.

[0059] The number of the test models is the same as the number of the corresponding relationships of the expansion design sub-models.

[0060] By integrating multiple models, the present invention can make full use of historical settlement data and initial monitoring data, improve the accuracy and reliability of settlement prediction, help to identify potential settlement risks in advance, and provide a basis for timely adjustment of construction strategies; secondly, by establishing an objective function and optimizing various adjustment methods, the present invention can quickly determine the optimal adjustment method after predicting the settlement trend, realize real-time compensation and adjustment of settlement, help to reduce the settlement amount during the construction process, and ensure the construction safety and the stability of the surrounding environment; in addition, the method provided by the present invention can flexibly adjust construction parameters according to different geological conditions, construction environments and settlement prediction results, realize personalized settlement control schemes, enhance the flexibility and adaptability of the construction process, and help to cope with complex and changeable construction challenges. Through accurate prediction and timely adjustment, the present invention helps to reduce construction accidents and damage to the surrounding environment caused by settlement, thereby reducing construction risks and costs. At the same time, the optimized construction scheme also helps to improve construction efficiency and shorten the construction period.

[0061] Specifically, the geological exploration data includes soil type, soil density and soil internal friction angle;

[0062] The on-site monitoring data includes subway settlement monitoring data, horizontal displacement at the top of the retaining pile and vertical displacement at the top of the retaining pile;

[0063] The expansion design data includes foundation pit size and retaining structure form.

[0064] In this embodiment, the soil type can be determined by on-site observation or laboratory analysis. For example, soil samples are collected and taken back to the laboratory for particle analysis tests, and sieving method, hydrometer method, etc. are used to determine the content of particles with different particle sizes in the soil, so as to determine the soil type according to relevant standards.

[0065] The soil density can be determined by methods such as the cutting ring method or the nuclear densitometer method. The cutting ring method involves taking soil samples with a cutting ring on-site, weighing the mass and volume of the soil in the cutting ring, calculating the wet density of the soil, and then measuring the soil moisture content by the drying method to further calculate the dry density. The nuclear densitometer method utilizes the interaction between the rays emitted by the nuclear densitometer and the soil, and determines the soil density based on the absorption and scattering of the rays.

[0066] The soil internal friction angle can be determined by methods such as the direct shear test or the triaxial compression test. The direct shear test is to place the soil sample in a direct shear apparatus in the laboratory, apply vertical pressure and horizontal shear force, measure the shear strength of the soil sample under different vertical pressures, and calculate the soil internal friction angle according to Coulomb's law. The triaxial compression test is to apply confining pressure and axial pressure to a cylindrical soil sample, causing the soil sample to undergo shear failure under triaxial stress conditions, and determining the soil internal friction angle through the stress-strain relationship and Mohr-Coulomb strength theory.

[0067] The subway settlement monitoring data can be obtained through methods such as leveling or GPS measurement. Leveling involves setting settlement monitoring points on the subway, using a level and a leveling staff, and calculating the settlement amount of the monitoring points by measuring the change in elevation difference between the monitoring points and the known elevation points. GPS measurement utilizes a Global Positioning System (GPS) receiver to receive satellite signals, determine the three-dimensional coordinates of the monitoring points, and obtain subway settlement data by regularly observing the coordinate changes, which has advantages such as high accuracy and fast speed.

[0068] The horizontal displacement at the top of the retaining pile can be obtained by total station measurement or inclinometer. Set a reference point at a stable position away from the retaining pile, use a total station to measure the horizontal angle and distance changes between the monitoring point at the top of the retaining pile and the reference point, and calculate the horizontal displacement amount; or pre-bury an inclinometer tube in the retaining pile, place the inclinometer probe into the inclinometer tube, measure the inclination angles at different depths, and obtain the horizontal displacement at the top of the retaining pile through data processing.

[0069] The method for obtaining the vertical displacement at the top of the retaining pile is the same as the method for obtaining subway settlement monitoring data, and methods such as leveling or GPS measurement can be used to obtain the displacement change data of the top of the retaining pile in the vertical direction.

[0070] The tunnel dimensions can be directly obtained from the design drawings or on-site measurement.

[0071] The form of the support structure can be determined through design calculations: According to factors such as the geological conditions, burial depth, dimensions, and usage requirements of the tunnel, through mechanical calculations and analyses, determine the appropriate form of the support structure, such as shotcrete support, steel arch support, lining support, etc.

[0072] In this embodiment, the above data acquisition methods are prior arts, and the methods and processes for data acquisition are not specifically limited and will not be elaborated herein.

[0073] Specifically, the geological exploration sub-model includes a soil type sub-model, a soil density sub-model, and a soil internal friction angle sub-model;

[0074] The expansion design sub-model includes a foundation pit size sub-model and a support structure sub-model.

[0075] In this embodiment, any change in the sub-data of the expansion design data and the geological exploration data will cause a change in the on-site monitoring data under the same construction conditions.

[0076] It can be understood that the expansion design data and the geological exploration data are the basic information for subway construction. Any change in these data will directly affect the actual performance during the construction process, and thus cause a change in the on-site monitoring data.

[0077] Regarding the change in soil type, different types of soil have different mechanical properties. For example, cohesive soil and sandy soil have significant differences in shear strength, compressibility, permeability, etc. If the soil type changes (such as from sandy soil to clay soil), the deformation characteristics, settlement rate, and settlement amount of the soil mass during the construction process will change. For example, the settlement of sandy soil is usually faster but the total amount is smaller, while the settlement of clay soil may be slower but the total amount is larger. Therefore, the subway settlement monitoring data and the underground pipeline settlement monitoring data will show different settlement trends and rates due to the change in soil type.

[0078] Regarding the change in soil density, soil density is related to the compaction degree and particle arrangement of the soil mass, and directly affects the bearing capacity and anti-deformation ability of the soil mass. Soils with higher density usually have higher shear strength and lower compressibility, and vice versa, they are prone to deformation and settlement. If the soil density decreases (such as due to construction disturbance or groundwater immersion), the horizontal displacement and vertical displacement of the support pile will increase, and the subway settlement will also increase.

[0079] Regarding the change in soil internal friction angle, the internal friction angle is an index to measure the friction force between soil particles, and directly affects the shear strength of the soil mass. The larger the internal friction angle, the higher the shear strength of the soil mass and the better the stability; conversely, the soil mass is more prone to shear deformation and settlement. If the internal friction angle decreases, the stability of the soil mass decreases, resulting in an increase in the deformation of the support structure, and the subway settlement and the underground pipeline settlement will also increase.

[0080] The expansion design data includes tunnel size, tunnel depth, support structure form, etc. These data determine the mechanical environment and construction method during the construction process.

[0081] Regarding the change in tunnel size, the cross-sectional size of the tunnel directly affects the range and degree of soil disturbance during construction. If the tunnel size is large, the range of disturbance to the surrounding soil during construction will also increase, resulting in greater ground deformation and settlement. The increase in tunnel size leads to an increase in subway settlement and underground pipeline settlement, and the displacement of the retaining piles also increases.

[0082] Regarding the change in the form of the support structure, the form of the support structure determines the supporting capacity and stability of the soil during construction. Different forms of support structures (such as anchor rod support, steel support, concrete support, etc.) have different constraint capabilities for the soil. If the form of the support structure changes, the stability of the soil will also change accordingly. If the form of the support structure changes from steel support to anchor rod support, the horizontal and vertical displacements of the retaining piles may change, and the subway settlement may also increase or decrease due to the change in the supporting capacity. Different support structures have different settlement impacts as the construction process progresses.

[0083] Please refer to Figure 2 as shown, which is a flowchart for determining the corresponding relationship in an embodiment of the present invention. In step S3, determining the corresponding relationship between each geological exploration sub-model and the expanded design sub-model includes:

[0084] Step S31, according to the historical settlement data, perform data sequence accumulation on a single type of geological exploration sub-model to obtain the corresponding geological exploration update data sequence;

[0085] Step S32, according to the same historical settlement data, perform data sequence accumulation on a single type of expanded design sub-model to obtain the corresponding expanded design update data sequence;

[0086] Step S33, calculate the influence degree of the geological exploration update data sequence and the expanded design update data sequence;

[0087] Step S34, repeat step S33 to determine the influence degree of each geological exploration sub-model and each expanded design sub-model, and determine the corresponding relationship between each geological exploration sub-model and the expanded design sub-model according to the influence degree of the geological exploration update data sequence and the expanded design update data sequence.

[0088] In implementation, in step S31, data sequence accumulation is performed on a single type of geological exploration sub-model to obtain a corresponding geological exploration update data sequence. The process includes: selecting a type of geological exploration sub-model (such as a soil type sub-model, a soil density sub-model, etc.), and accumulating the data related to this sub-model (such as soil type data, soil density data at different monitoring points or different times, etc.) in a certain order (construction time order) to obtain an updated data sequence. This sequence reflects the overall characteristics or change trends of this geological exploration sub-model within the accumulation range, and is called the geological exploration update data sequence.

[0089] In step S32, based on the same historical settlement data, data sequence accumulation is performed on a single type of expansion design sub-model to obtain a corresponding expansion design update data sequence. The process includes: selecting a type of expansion design sub-model (such as a foundation pit size sub-model, a support structure sub-model, etc.), and based on the same historical settlement data, accumulating the data related to this expansion design sub-model (such as settlement data under different foundation pit sizes, settlement data under different support structure forms, etc.) in the same order (construction time order) to obtain an updated data sequence. This sequence reflects the overall characteristics or change trends of this expansion design sub-model within the accumulation range, and is called the expansion design update data sequence.

[0090] In step S33, calculate the influence degree between the geological exploration update data sequence and the expansion design update data sequence. The process includes: selecting a geological exploration update data sequence and an expansion design update data sequence, quantifying the correlation degree or mutual influence degree between these two update data sequences. The calculation result is the influence degree between this geological exploration sub-model and this expansion design sub-model, which reflects the combined influence degree of geological exploration factors and expansion design factors on settlement.

[0091] In step S34, repeat step S33 to determine the influence degree of each geological exploration sub-model and each expansion design sub-model. The process of determining the corresponding relationship between each geological exploration sub-model and the expansion design sub-model according to the influence degree between each geological exploration update data sequence and the expansion design update data sequence includes,

[0092] For each geological exploration sub-model, repeat the above process of calculating the influence degree with the expansion design sub-model. At the same time, for each expansion design sub-model, also repeat the process of calculating the influence degree with the geological exploration sub-model. Through multiple calculations and comparisons, determine the corresponding relationship between each geological exploration sub-model and the expansion design sub-model. This corresponding relationship indicates that in terms of settlement influence, the sub-data in geological exploration data and the sub-data in expansion design data have a relatively high degree of mutual influence, providing a basis for subsequent settlement prediction and compensation.

[0093] The degree of influence is determined according to the following formula: where xi is the geological exploration sub-type data, and yi is the expansion design sub-model data. is the average value of the geological exploration sub-type data, is the average value of the expansion design sub-model data, i is an integer greater than 0, and n is the total number of data in the on-site monitoring data sub-type.

[0094] The Pearson correlation coefficient is a statistic that measures the degree of correlation between two variables, and its value range is [-1, 1]. The closer the absolute value of the correlation coefficient is to 1, the higher the degree of linear correlation between the two variables; the closer the absolute value is to 0, the lower the degree of linear correlation.

[0095] Generally speaking, the value of the Pearson correlation coefficient can be interpreted according to the following criteria: 0.8 - 1.0 indicates a very strong correlation; 0.6 - 0.8 indicates a strong correlation.

[0096] In this embodiment, the expansion design data sub-types with an influence degree greater than 0.6 and the corresponding geological exploration data sub-types are recorded as a set of corresponding relationships, that is, the corresponding relationships between each expansion design data sub-type and each geological exploration data sub-type are not unique.

[0097] It can be understood that the corresponding relationship represents the influence degree of each geological exploration data type and each expansion design data on the settlement during the expansion process under the same settlement conditions.

[0098] In the present invention, during the subway double-sided expansion construction, the influence of geological exploration data and construction design parameters on the settlement is interrelated. Through historical settlement data, data sequence accumulation is performed on the geological exploration sub-model and the expansion design sub-model, the influence degree of the two is calculated, and variable combinations with relatively high correlation are screened out. The linear correlation between the geological exploration data and the construction design parameters is calculated through the Pearson correlation coefficient. When the correlation coefficient is greater than 0.6, it indicates that the influence degree of the two on the settlement is relatively high, and they can be used as a set of corresponding relationships. This corresponding relationship indicates that under the same settlement conditions, the combination of specific geological exploration data types and construction design parameters has a relatively high influence degree on the settlement. When establishing a test model, variables can be screened according to conditions such as the influencing factors in the sub-models included in the corresponding relationship, which can ensure that the variables included in the model are all important factors closely related to the settlement, improve the reliability and effectiveness of the prediction model, make the prediction results better reflect the actual settlement situation, and provide more accurate guidance for the real-time compensation of the project and subsequent construction.

[0099] Please refer to Figure 3 As shown, it is a flowchart for determining the influencing factors in an embodiment of the present invention. In step S4, the influencing factors of the variables in the sub-model of the current corresponding relationship on the on-site monitoring data include:

[0100] Determine the test variables in the current sub-model, take them and the variables as constants, adjust the test variables, and obtain the actual values of each on-site monitoring data and the change amounts of each time in the historical settlement data, and calculate the influence factors of the current test variables on the on-site monitoring data;

[0101] Adjust the test variables and constants in the current sub-model, and determine again the influence factors of the new test variables on the on-site monitoring data;

[0102] Repeat the process of replacing variables to calculate the influence factors until all the influence factors of the variables in the sub-model of the current corresponding relationship are determined.

[0103] In practice, the influence of soil-related data and tunnel construction parameters on settlement interacts. The mechanical properties of the soil (such as type, density, internal friction angle, and water content) determine the degree of response of the soil mass to construction disturbances, while the size, burial depth, and support structure form of the tunnel directly affect the range and degree of construction disturbances to the soil mass.

[0104] The influence of the soil internal friction angle and tunnel burial depth on settlement. The smaller the soil internal friction angle, the lower the shear strength of the soil mass, and the higher the settlement risk; the shallower the tunnel burial depth, the more significant the impact of construction on the subway, the greater the settlement amount. Soils with a lower internal friction angle (such as clay) are more likely to undergo shear deformation when the stress changes, and when constructing a shallow-buried tunnel, the stress redistribution of the soil mass more directly affects the subway, resulting in increased settlement.

[0105] The influence of soil type and support structure form on settlement. Cohesive soil (such as clay) is more likely to settle than sandy soil; when the support structure form is unreasonable, the settlement risk increases significantly. The shear strength of cohesive soil is sensitive to changes in moisture and stress, and it has a relatively high compressibility, and is prone to large settlement during the construction process.

[0106] The influence of soil density and tunnel construction parameters on settlement. The lower the soil density, the weaker the bearing capacity of the soil mass, and the higher the settlement risk; when the tunnel construction parameters (such as size, burial depth) are unreasonable, the settlement risk increases significantly. Low-density soil masses usually have a low shear strength and a high compressibility, and are prone to deformation and settlement under construction disturbances. Changes in tunnel size and burial depth will directly affect the range and degree of stress disturbances of construction to the soil mass.

[0107] In practice, soil types have essential differences in physical and mechanical properties, which are the fundamental factors determining tunnel settlement. For example, soft soils and silty soils have characteristics such as high compressibility and low strength. During and after tunnel construction, they will produce significant settlement, and this settlement often lasts for a long time and is difficult to stabilize. For large-sized tunnels excavated in soft soil strata, due to the large excavation cross-section, the disturbance range and degree of the soft soil are very large, and the stress release and redistribution of the soil mass are more complex, resulting in significant settlement. Even if the same support structure form is adopted, tunnels in soft soil are more likely to have excessive settlement or even failure of the support structure. On the other hand, like hard rock-like soils, which have high strength, high stiffness, and good stability, can provide a better bearing foundation for the tunnel. Regardless of how the tunnel size, burial depth changes, or what support structure form is adopted, its settlement amount is usually small.

[0108] Therefore, for each test model, there are usually multiple variables that affect the in-situ monitoring data;

[0109] For example, the variables included in the current design sub-model are the soil internal friction angle, soil type, and the influence of tunnel burial depth on settlement.

[0110] In the specific implementation process, when manufacturing the test environment for each variable, the variables of the design sub-model are superimposed separately to obtain the influence factors of each variable on various types of in-situ monitoring data.

[0111] Specifically, in the step S6, determining the first settlement prediction model according to the prediction results includes:

[0112] Using each test model to predict all types of in-situ monitoring data;

[0113] According to the prediction results of each in-situ monitoring data, the test model corresponding to the smallest prediction error of each in-situ monitoring data is selected as the first settlement prediction model.

[0114] In the implementation, for one type of in-situ monitoring data, if the simulation results of two or more test models are the same, the test model with the smallest overall error is determined through other types of in-situ monitoring data.

[0115] In this embodiment, when establishing the test model, it is established according to the variables in the sub-model included in the corresponding relationship, where the influence factor ratio is higher than the average value and the corresponding variable quantity is greater than 1 / 4 of the total.

[0116] It is understandable that variables with an influence factor ratio higher than the average have a greater impact on on-site monitoring data and are important factors leading to phenomena such as settlement. Selecting these variables to establish a test model can make the model more accurately reflect the actual situation, reduce errors caused by interference from secondary factors, and thus more precisely predict situations such as settlement during the bilateral expansion of the subway. The corresponding number of variables being greater than 1 / 4 of the total ensures that a sufficient number of important variables are included in the model, avoiding the situation of only considering a few factors and ignoring other important influences. This enables the model to comprehensively consider the impacts of various factors on on-site monitoring data from multiple perspectives, more comprehensively simulate the actual physical process, and further improve the accuracy and reliability of the model. At the same time, it reduces the amount of calculation and data processing, improves the operating efficiency and calculation speed of the model, enables the model to give prediction results more quickly, and meets the requirements for timeliness in actual application scenarios such as real-time compensation. During the model optimization process, these key variables can be adjusted and improved more targeted, improving the optimization efficiency of the model, more quickly finding the optimal model parameters and structure, and enhancing the overall performance of the model.

[0117] In addition, overfitting can be avoided. If only a very small number of variables with extremely high influence factors are selected to establish a model, it will cause the model to be overly dependent on these specific variables, overfit the training data, and perform poorly when facing new and unseen data. A combination of variables with an influence factor ratio higher than the average and a sufficient number can form a relatively balanced relationship in the model. Different variables restrict and influence each other and jointly act on the prediction of on-site monitoring data. This balanced relationship helps the model to give reasonable prediction results in various situations and will not cause large fluctuations in the model output due to abnormal changes in a certain variable, thus enhancing the stability of the model.

[0118] Specifically, in the step S6, adjusting the influence weights of the variables in each test model of the first settlement prediction model includes:

[0119] Adjusting the influence weights of the variables in the first settlement prediction model according to the principle of minimizing the simulation error;

[0120] The principle of minimizing the simulation error means that the error rate of the prediction result of the first settlement prediction model determined by using the adjusted influence weights of the variables is less than the error rate of the prediction result of any first settlement prediction model determined by the influence weights of the variables other than the adjusted ones.

[0121] In implementation, when constructing the first settlement prediction model, the error rate of the model prediction result determined by adjusting the influence weight of variables should be less than the error rate of the model prediction result when determining the influence weight of variables by any other non-current adjustment method. That is, among all possible methods for determining the influence weight of variables, the adjusted influence weight of variables can minimize the prediction error of the first settlement prediction model.

[0122] The principle of minimum error rate can obtain the influence weight of variables through data exhaustion and historical variation. The acquisition method is not unique, and an appropriate weight value calculation method can be selected according to needs under the existing data processing technology conditions, which will not be elaborated here.

[0123] Specifically, in step S7, if the subway settlement prediction value exceeds the standard prediction value, the test model with the minimum prediction error among each on-site monitoring data is selected for separate prediction, and the maximum prediction value of each on-site monitoring data is selected as the final subway settlement prediction value, and the backfill and axial force servo system are used for compensation.

[0124] In implementation, the standard prediction value is determined according to the maximum on-site monitoring data without additional compensation under the same expansion design data and geological exploration data in the historical settlement data.

[0125] Please refer to Figure 4 As shown, it is a schematic structural diagram of the excavation real-time compensation system for subway double-sided expansion in an embodiment of the present invention. The present invention also provides a system for an excavation settlement value prediction method applied to subway double-sided expansion, including:

[0126] An information collection module, which is used to collect the initial monitoring data of the subway expansion area, and obtain the historical settlement data and expansion design data of the subway expansion area;

[0127] An information processing module, which is connected to the information collection module, and is used to establish the geological exploration sub-model and the expansion design sub-model, determine the influence degree of each geological exploration sub-model on the expansion design sub-model, and determine the corresponding relationship between each geological exploration sub-model and the expansion design sub-model;

[0128] A model establishment module, which is respectively connected to the information collection module and the information processing module, and is used to establish a test environment for superimposing various variables, determine the influence factors of variables in each sub-model on the on-site monitoring data, establish several test models and perform simulation predictions to determine the first settlement prediction model;

[0129] A model optimization module, which is respectively connected to the information collection module and the model establishment module, is used to analyze and predict the first settlement prediction model based on historical settlement data, and adjust the influence weights of various variables in each test model of the first settlement prediction model according to the gap between the prediction result and the on-site monitoring data, so as to obtain a second settlement prediction model;

[0130] A compensation module, which is respectively connected to the model optimization module and the information collection module, is used to compensate according to the subway settlement prediction value simulated by the second settlement prediction model. Embodiment 1

[0131] The soil type is an interlayer of silty clay and silty sand, with a local interlayer of mucky silty clay. The average density of silty clay is 1.9 g / cm³, the internal friction angle is 18°, the average density of silty sand is 2.0 g / cm³, the internal friction angle is 30°, the water content is 20%, the average density of mucky silty clay is 1.75 g / cm³, the internal friction angle is 12°, and the water content is 35%.

[0132] The expansion design data is that the total width of the tunnel after bilateral widening is 12 m and the height is 7 m, and a support system of bored cast-in-place piles with a diameter of 1000 mm and a spacing of 1500 mm plus steel pipe supports and internal supports is adopted.

[0133] The on-site monitoring data is that the maximum subway settlement is 35 mm, the settlement rate in some areas is 2 mm / d, the maximum underground pipeline settlement is 25 mm, the settlement rate is 1.5 mm / d, the maximum horizontal displacement at the top of the support pile is 18 mm, the displacement rate is 0.8 mm / d, the maximum vertical displacement is 10 mm, and the displacement rate is 0.5 mm / d;

[0134] The compensation method is to reduce the original designed earth excavation depth of 3 m per layer to 2 m; use a dewatering well to lower the original groundwater level of 3 m to 5 m, so that the groundwater depth is 2 m below the bottom of the tunnel; increase the backfill depth from the original designed 1 m to 1.5 m on both sides and the top of the tunnel, and use graded sand and gravel for backfill; to control the displacement of the support pile, install an axial force servo system on the steel pipe support, and automatically increase the axial force by 100 kN when the horizontal displacement at the top of the support pile exceeds 15 mm, and increase the axial force by 50 kN when the vertical displacement exceeds 8 mm.

[0135] After implementing these compensation measures, the subway settlement rate is reduced to below 1 mm / d, the underground pipeline settlement rate is reduced to below 0.8 mm / d, and the horizontal and vertical displacements at the top of the support pile are basically stable, meeting the engineering safety and use requirements.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting 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 for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for predicting the excavation settlement value of bilateral expansion of the subway, characterized in that, include: Step S1, collecting initial monitoring data of the subway extension area, and obtaining historical settlement data and extension design data of the subway extension area, the initial monitoring data including geological survey data and on-site monitoring data; Step S2, constructing a single model based on the individual data types of the initial monitoring data, and establishing several geological survey sub-models and construction design sub-models respectively; Step S3, determining the degree of influence of each geological survey sub-model on the extension and construction design sub-model, and determining the corresponding relationship between each geological survey sub-model and the extension and construction design sub-model according to each degree of influence; Step S4, in the models included in the same corresponding relationship, a test environment is established in which each variable is superimposed, and the influence factor of the variable in each sub-model on the field monitoring data is determined; Step S5, re-establishing several test models according to the impact degree and impact factor, and performing simulation prediction on each field monitoring data, and determining a first settlement prediction model according to the prediction results; Step S6, using the historical settlement data to analyze and predict the first settlement prediction model, and adjusting the influence weight of each variable in each test model in the first settlement prediction model according to the difference between the prediction result and the field monitoring data, to obtain a second settlement prediction model; Step S7, using the second settlement prediction model to perform simulation to obtain a subway settlement prediction value, and performing compensation according to the subway settlement prediction value; The geological survey data includes soil type, soil density and soil internal friction angle; The on-site monitoring data includes subway settlement monitoring data, horizontal displacement of the top of the support pile, and vertical displacement of the top of the support pile; The expansion design data includes foundation pit dimensions and support structure form; In step S5, determining the first settlement prediction model according to the prediction result includes: Use each tested model to predict all types of field monitoring data; According to the prediction results of each field monitoring data, the test model corresponding to the smallest prediction error of each field monitoring data is screened out as the first settlement prediction model.

2. The prediction method of excavation settlement value for double-sided expansion of subway according to claim 1, characterized in that The geological survey sub-model includes a soil type sub-model, a soil density sub-model, and a soil internal friction angle sub-model; The expansion design sub-model includes a foundation pit size sub-model and a support structure sub-model.

3. A method for predicting the excavation settlement value of double-sided expansion of a subway, according to claim 1, characterized in that, In step S3, determining the correspondence between each geological survey sub-model and the expansion design sub-model includes: Step S31, accumulating data sequences of a single type of geological survey sub-model based on historical settlement data to obtain a corresponding geological survey update data sequence; Step S32: accumulating data sequences for a single type of extension and construction design sub-model based on the same historical settlement data to obtain a corresponding extension and construction design update data sequence; Step S33, calculating the influence degree between the geological survey update data sequence and the expansion design update data sequence; Step S34, repeat step S33, determine the influence degree of each geological survey sub-model and each extension design sub-model, and determine the correspondence between each geological survey sub-model and the extension design sub-model according to the influence degree of each geological survey update data sequence and the extension design update data sequence.

4. A prediction method for excavation settlement values in the bilateral expansion of a subway, according to claim 1, characterized in that In the step S4, the influence factors of the variables in the sub-model of the current correspondence relationship on the on-site monitoring data include: Determine the test variables in the current sub-model, take the remaining variables as constants, adjust the test variables, and obtain the actual values of the on-site monitoring data and the change amounts at each time in the historical settlement data, and calculate the influence factors of the current test variables on the on-site monitoring data; Adjust the test variables and constants in the current sub-model, and re-determine the influence factors of the new test variables on the on-site monitoring data; Repeat the process of replacing variables to calculate the influence factors until the influence factors of all variables in the sub-model of the current correspondence relationship are determined.

5. A prediction method for excavation settlement values in the bilateral expansion of a subway, according to claim 1, characterized in that In the step S6, adjusting the influence weights of the variables in each test model of the first settlement prediction model includes: Adjust the influence weights of the variables in the first settlement prediction model according to the principle of minimizing the simulation error; The principle of minimizing the simulation error is that the error rate of the prediction result of the first settlement prediction model determined by the influence weights of the adjusted variables is the smallest.

6. A prediction method for excavation settlement values in the bilateral expansion of a subway, according to claim 1, characterized in that In the step S7, if the subway settlement prediction value exceeds the standard prediction value, select the test models with the smallest prediction errors in each on-site monitoring data for separate predictions, and select the maximum prediction value of each on-site monitoring data as the final subway settlement prediction value, and use the backfill and axial force servo system for compensation.

7. A system for predicting the excavation settlement value of the subway bilateral expansion described in any one of claims 1-6, characterized in that, Including: An information collection module for collecting the initial monitoring data of the subway expansion area, and obtaining the historical settlement data and expansion design data of the subway expansion area; An information processing module connected to the information collection module for establishing the geological exploration sub-model and the expansion design sub-model, determining the influence degree of each geological exploration sub-model on the expansion design sub-model, and determining the correspondence relationship between each geological exploration sub-model and the expansion design sub-model; A model establishment module connected to the information collection module and the information processing module respectively for establishing a test environment for superimposing various variables, determining the influence factors of the variables in each sub-model on the on-site monitoring data, establishing several test models and performing simulation predictions to determine the first settlement prediction model; A model optimization module connected to the information collection module and the model establishment module respectively for analyzing and predicting the first settlement prediction model according to the historical settlement data, and adjusting the influence weights of the variables in each test model of the first settlement prediction model according to the gap between the prediction result and the on-site monitoring data to obtain the second settlement prediction model; A compensation module connected to the model optimization module and the information collection module respectively for compensating according to the subway settlement prediction value simulated by the second settlement prediction model.

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